Customize DLA unsupported layers

I wonder is there a way I can customize unsupported layers on DLA?

There are multiple documents showing that I can customize CUDA/Tensor Cores for TensorRT e.g. Extending TensorRT with Custom Layers.

And there are some plugin examples in TensorRT Plugins.

Hi,

Sorry for the late update.

Which layer do you need?
Since DLA is a hardware engine, please check the below document to see if your layer can be supported or not first:

Thanks.

Hi,

Sorry for the late reply.

I want to deploy an Unet-like neural network to DLA, e.g. from the official Unet repo.

There are two upscaling options from the network, one is to use a resize layer to perform a bilinear upscaling, and another is to use a deconvolution layer(ConvTranspose2d) to perform the upscaling.

I have tested several times that the performance between these two layers is quite difference. DLA does not support the resize layer in Unet but deconvolution.

I wonder is there a way I can customize unsupported layers on DLA? Or DLA is the fixed-function hardware that accelerates specific deep-learning layers, I can not customize unsupported layers.

Thank you.

Hi,

You can find the details below. For a resize layer, DLA only supports integer scaling.

https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#dla-lay-supp-rest

The last two elements in scales, representing the scale values along height and width dimensions, respectively, must be integer values in the range of [1, 32] in nearest-neighbor mode and [1, 4] in bilinear mode.

Does your model meet the requirements?
Thanks.

The official Unet model does not meet the requirements. I will use deconvolution instead of bilinear interpolation.

Thanks.

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